submission = pd.read_csv("./sample_submission.csv") # columns = [id,label]
# preprocess batch images & do prediction
if useTTA == "True":
print("[INFO] applying TTA..")
else:
print("[INFO] NOT applying TTA..")
# loop over batches
for i in tqdm(range(0, N, B)):
batchPaths = imagePaths[i:i + B]
batchImages = []
for path in batchPaths:
image = cv2.imread(path)
image = aap.preprocess(image) # maintain AR and resize to 256 x 256
image = iap.preprocess(image)
# Special for ImageNet dataset => substracting mean RGB pixel intensity
image = imagenet_utils.preprocess_input(image) # (256, 256, 3)
batchImages.append(image)
pass
if useTTA == "True":
for image in batchImages:
crops = cp1.preprocess(image)
# predict over 10 crops
crops_probs = model.predict(crops)
# predict probs of dogs
pred = crops_probs.mean(axis=0)[1]
predictions.append(pred)
print("[INFO] rank-1 accuracy = {:.2f}%".format(rank1acc1 * 100))
testGen1.close()
print("[INFO] evaluating on valset WITH crop/TTA ...")
testGen2 = HDF5DatasetGenerator(
config.TEST_HDF5,
BATCH_SIZE,
# substract mean
preprocessors=[mp],
classes=2,
)
predictions2 = []
# loop over batchs & apply 10-crops oversampling/TTA
for images, labels in tqdm(testGen2.generator(passes=1)):
# loop over image, create 10-crops, convert to keras array
for image in images:
crops = cp.preprocess(image)
crops = np.array([iap.preprocess(crop) for crop in crops], dtype="float32")
# predict over 10 crops
preds = model.predict(crops)
predictions2.append(preds.mean(axis=0))
pass
rank1acc2, _ = rank5_accuracy(predictions2, testGen2.db["labels"])
print("[INFO] rank-1 accuracy = {:.2f}%".format(rank1acc2 * 100))
testGen2.close()
